In this work, we propose a domain generalization (DG) approach to learn on
several labeled source domains and transfer knowledge to a target domain that
is inaccessible in training. Considering the inherent conditional and label
shifts, we would expect the alignment of $p(x|y)$ and $p(y)$. However, the
widely used domain invariant feature learning (IFL) methods relies on aligning
the marginal concept shift w.r.t. $p(x)$, which rests on an unrealistic
assumption that $p(y)$ is invariant across domains. We thereby propose a novel
variational Bayesian inference framework to enforce the conditional
distribution alignment w.r.t. $p(x|y)$ via the prior distribution matching in a
latent space, which also takes the marginal label shift w.r.t. $p(y)$ into
consideration with the posterior alignment. Extensive experiments on various
benchmarks demonstrate that our framework is robust to the label shift and the
cross-domain accuracy is significantly improved, thereby achieving superior
performance over the conventional IFL counterparts.

本文提出了一种领域泛化（DG）方法来学习多个标记源域并将知识转移至在训练中不可访问的目标领域，使用变分贝叶斯推断框架来强制进行条件分布对齐，并在考虑后验分布对齐的同时考虑与 $p（y）$ 相关的边际标签偏移，从而在各个基准测试上实现了优越的表现。